文章目录
前言
论文地址
YOLOv7源码
下面对v0.1版本的整体网络结构及各个组件，结合源码和
train
文件夹中的
yolov7.yaml
配置文件进行解析。
整体网络结构
分解的yolov7.yaml
# parametersnc:80# number of classesdepth_multiple:1.0# model depth multiplewidth_multiple:1.0# layer channel multiple# anchorsanchors:-[12,16,19,36,40,28]# P3/8-[36,75,76,55,72,146]# P4/16-[142,110,192,243,459,401]# P5/32# yolov7 backbonebackbone:# [from, number, module, args][[-1,1, Conv,[32,3,1]],# 0[-1,1, Conv,[64,3,2]],# 1-P1/2 [-1,1, Conv,[64,3,1]],[-1,1, Conv,[128,3,2]],# 3-P2/4# ELAN1[-1,1, Conv,[64,1,1]],[-2,1, Conv,[64,1,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[[-1,-3,-5,-6],1, Concat,[1]],[-1,1, Conv,[256,1,1]],# 11# MPConv[-1,1, MP,[]],[-1,1, Conv,[128,1,1]],[-3,1, Conv,[128,1,1]],[-1,1, Conv,[128,3,2]],[[-1,-3],1, Concat,[1]],# 16-P3/8# ELAN1[-1,1, Conv,[128,1,1]],[-2,1, Conv,[128,1,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[[-1,-3,-5,-6],1, Concat,[1]],[-1,1, Conv,[512,1,1]],# 24# MPConv[-1,1, MP,[]],[-1,1, Conv,[256,1,1]],[-3,1, Conv,[256,1,1]],[-1,1, Conv,[256,3,2]],[[-1,-3],1, Concat,[1]],# 29-P4/16# ELAN1[-1,1, Conv,[256,1,1]],[-2,1, Conv,[256,1,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[[-1,-3,-5,-6],1, Concat,[1]],[-1,1, Conv,[1024,1,1]],# 37# MPConv[-1,1, MP,[]],[-1,1, Conv,[512,1,1]],[-3,1, Conv,[512,1,1]],[-1,1, Conv,[512,3,2]],[[-1,-3],1, Concat,[1]],# 42-P5/32# ELAN1[-1,1, Conv,[256,1,1]],[-2,1, Conv,[256,1,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[[-1,-3,-5,-6],1, Concat,[1]],[-1,1, Conv,[1024,1,1]],# 50]# yolov7 headhead:[[-1,1, SPPCSPC,[512]],# 51[-1,1, Conv,[256,1,1]],[-1,1, nn.Upsample,[None,2,'nearest']],[37,1, Conv,[256,1,1]],# route backbone P4[[-1,-2],1, Concat,[1]],# ELAN2[-1,1, Conv,[256,1,1]],[-2,1, Conv,[256,1,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[[-1,-2,-3,-4,-5,-6],1, Concat,[1]],[-1,1, Conv,[256,1,1]],# 63[-1,1, Conv,[128,1,1]],[-1,1, nn.Upsample,[None,2,'nearest']],[24,1, Conv,[128,1,1]],# route backbone P3[[-1,-2],1, Concat,[1]],# ELAN2[-1,1, Conv,[128,1,1]],[-2,1, Conv,[128,1,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[[-1,-2,-3,-4,-5,-6],1, Concat,[1]],[-1,1, Conv,[128,1,1]],# 75# MPConv Channel × 2[-1,1, MP,[]],[-1,1, Conv,[128,1,1]],[-3,1, Conv,[128,1,1]],[-1,1, Conv,[128,3,2]],[[-1,-3,63],1, Concat,[1]],# ELAN2[-1,1, Conv,[256,1,1]],[-2,1, Conv,[256,1,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[[-1,-2,-3,-4,-5,-6],1, Concat,[1]],[-1,1, Conv,[256,1,1]],# 88# MPConv Channel × 2[-1,1, MP,[]],[-1,1, Conv,[256,1,1]],[-3,1, Conv,[256,1,1]],[-1,1, Conv,[256,3,2]],[[-1,-3,51],1, Concat,[1]],# ELAN2[-1,1, Conv,[512,1,1]],[-2,1, Conv,[512,1,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[-1,1, Conv,[256,3,1]],[[-1,-2,-3,-4,-5,-6],1, Concat,[1]],[-1,1, Conv,[512,1,1]],# 101[75,1, RepConv,[256,3,1]],[88,1, RepConv,[512,3,1]],[101,1, RepConv,[1024,3,1]],[[102,103,104],1, IDetect,[nc, anchors]],# Detect(P3, P4, P5)]
各组件结构
ELAN1 (backbone)
yolov7.yaml中对应部分：
# ELAN1[-1,1, Conv,[64,1,1]],[-2,1, Conv,[64,1,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[-1,1, Conv,[64,3,1]],[[-1,-3,-5,-6],1, Concat,[1]],[-1,1, Conv,[256,1,1]],# 11
ELAN2 (head)
yolov7.yaml中对应部分：
# ELAN2[-1,1, Conv,[256,1,1]],[-2,1, Conv,[256,1,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[-1,1, Conv,[128,3,1]],[[-1,-2,-3,-4,-5,-6],1, Concat,[1]],[-1,1, Conv,[256,1,1]],# 63
MPConv
- 在
backnone中的对应部分 - 要注意相比于
MP函数之前，通道数减少一半
[-1,1, Conv,[256,1,1]],# 11# MPConv[-1,1, MP,[]],[-1,1, Conv,[128,1,1]],[-3,1, Conv,[128,1,1]],[-1,1, Conv,[128,3,2]],[[-1,-3],1, Concat,[1]],# 16-P3/8
- 在
head中的对应部分 - 要注意相比于
MP函数之前，通道数不变
[-1,1, Conv,[128,1,1]],# 75# MPConv Channel × 2[-1,1, MP,[]],[-1,1, Conv,[128,1,1]],[-3,1, Conv,[128,1,1]],[-1,1, Conv,[128,3,2]],[[-1,-3,63],1, Concat,[1]],
SPPCSPC
类似于
yolov5
中的
SPPF
，不同的是，使用了5×5、9×9、13×13最大池化。
common.py中对应部分：
classSPPCSPC(nn.Module):# CSP https://github.com/WongKinYiu/CrossStagePartialNetworksdef__init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5,9,13)):super(SPPCSPC, self).__init__()
c_ =int(2* c2 * e)# hidden channels
self.cv1 = Conv(c1, c_,1,1)
self.cv2 = Conv(c1, c_,1,1)
self.cv3 = Conv(c_, c_,3,1)
self.cv4 = Conv(c_, c_,1,1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x //2)for x in k])
self.cv5 = Conv(4* c_, c_,1,1)
self.cv6 = Conv(c_, c_,3,1)
self.cv7 = Conv(2* c_, c2,1,1)defforward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
y1 = self.cv6(self.cv5(torch.cat([x1]+[m(x1)for m in self.m],1)))
y2 = self.cv2(x)return self.cv7(torch.cat((y1, y2), dim=1))
RepConv（重参数卷积）
原理理解层面
- 训练时：一个33卷积、一个11卷积和一个BN层（当输入输出通道相同时）相加得到输出
- 推理时：将以上三部分重参数化，合并为一个3*3的卷积输出
代码实现层面
- 训练时：不执行
Model类的fuse函数 - 推理时：在
attempt_load函数加载训练好的模型时，会执行Model类的fuse函数，进而调用fuse_repvgg_block函数，实现将三个卷积重参数化，合并为一个卷积输出 common.py中对应部分：
# Represented convolution https://arxiv.org/abs/2101.03697classRepConv(nn.Module):'''重参数卷积
训练时：
deploy = False
rbr_dense(3*3卷积) + rbr_1x1(1*1卷积) + rbr_identity(c2 == c1时) 三者相加
rbr_reparam = None
推理时：
deploy = True
rbr_reparam = Conv2d
rbr_dense = None
rbr_1x1 = None
rbr_identity = None
'''def__init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):super(RepConv, self).__init__()
self.deploy = deploy
self.groups = g
self.in_channels = c1
self.out_channels = c2
assert k ==3assert autopad(k, p)==1
padding_11 = autopad(k, p)- k //2
self.act = nn.SiLU()if act isTrueelse(act ifisinstance(act, nn.Module)else nn.Identity())# 推理阶段，仅有一个3×3的卷积来替换if deploy:
self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)else:# 训练阶段，当输入和输出的通道数相同时，会在加一个BN层
self.rbr_identity =(nn.BatchNorm2d(num_features=c1)if c2 == c1 and s ==1elseNone)# 3×3的卷积(padding=1)
self.rbr_dense = nn.Sequential(
nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),
nn.BatchNorm2d(num_features=c2),)# 1×1的卷积
self.rbr_1x1 = nn.Sequential(
nn.Conv2d(c1, c2,1, s, padding_11, groups=g, bias=False),
nn.BatchNorm2d(num_features=c2),)defforward(self, inputs):ifhasattr(self,"rbr_reparam"):return self.act(self.rbr_reparam(inputs))if self.rbr_identity isNone:
id_out =0else:
id_out = self.rbr_identity(inputs)return self.act(self.rbr_dense(inputs)+ self.rbr_1x1(inputs)+ id_out)# Conv2D + BN -> Conv2Ddeffuse_conv_bn(self, conv, bn):
std =(bn.running_var + bn.eps).sqrt()
bias = bn.bias - bn.running_mean * bn.weight / std
t =(bn.weight / std).reshape(-1,1,1,1)
weights = conv.weight * t
bn = nn.Identity()
conv = nn.Conv2d(in_channels=conv.in_channels,
out_channels=conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
dilation=conv.dilation,
groups=conv.groups,
bias=True,
padding_mode=conv.padding_mode)
conv.weight = torch.nn.Parameter(weights)
conv.bias = torch.nn.Parameter(bias)return conv
# 在推理阶段才执行重参数操作deffuse_repvgg_block(self):if self.deploy:returnprint(f"RepConv.fuse_repvgg_block")
self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
rbr_1x1_bias = self.rbr_1x1.bias
# self.rbr_1x1.weight [256, 128, 1, 1]# weight_1x1_expanded [256, 128, 3, 3]
weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight,[1,1,1,1])# Fuse self.rbr_identityif(isinstance(self.rbr_identity, nn.BatchNorm2d)orisinstance(self.rbr_identity,
nn.modules.batchnorm.SyncBatchNorm)):# print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")
identity_conv_1x1 = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=1,
stride=1,
padding=0,
groups=self.groups,
bias=False)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
identity_conv_1x1.weight.data.fill_(0.0)
identity_conv_1x1.weight.data.fill_diagonal_(1.0)
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)
bias_identity_expanded = identity_conv_1x1.bias
weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight,[1,1,1,1])else:# print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")
bias_identity_expanded = torch.nn.Parameter(torch.zeros_like(rbr_1x1_bias))
weight_identity_expanded = torch.nn.Parameter(torch.zeros_like(weight_1x1_expanded))# print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")# print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")# print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")
self.rbr_dense.weight = torch.nn.Parameter(
self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)
self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)
self.rbr_reparam = self.rbr_dense
# 前向推理时，使用重参数化后的 rbr_reparam 函数
self.deploy =Trueif self.rbr_identity isnotNone:del self.rbr_identity
self.rbr_identity =Noneif self.rbr_1x1 isnotNone:del self.rbr_1x1
self.rbr_1x1 =Noneif self.rbr_dense isnotNone:del self.rbr_dense
self.rbr_dense =None
ImpConv（隐性知识学习）
这一部分直接继承自YOLOR中的显隐性知识学习。一般情况下，将神经网络的浅层特征称为显性知识，深层特征称为隐性知识。而YOLOR的作者（同时也是YOLOv7的作者）则直接把神经网络最终观察到的知识称为显性知识，那些观察不到、与观察无关的知识称为隐性知识。
在
model/common.py
文件中，定义了两类隐性知识：
ImplicitA
和
ImplicitM
，分别对输入 相加 和 相乘：
# AddclassImplicitA(nn.Module):def__init__(self, channel, mean=0., std=.02):super(ImplicitA, self).__init__()
self.channel = channel
self.mean = mean
self.std = std
# 全0矩阵
self.implicit = nn.Parameter(torch.zeros(1, channel,1,1))
nn.init.normal_(self.implicit, mean=self.mean, std=self.std)defforward(self, x):# 全0矩阵 与 输入 相加return self.implicit + x
# MultiplyclassImplicitM(nn.Module):def__init__(self, channel, mean=0., std=.02):super(ImplicitM, self).__init__()
self.channel = channel
self.mean = mean
self.std = std
# 全1矩阵
self.implicit = nn.Parameter(torch.ones(1, channel,1,1))
nn.init.normal_(self.implicit, mean=self.mean, std=self.std)defforward(self, x):# 全1矩阵 与 输入相乘return self.implicit * x
训练时
在模型训练阶段，先对输入进行
ImplicitA
操作， 在进行1*1卷积，最后进行
ImplicitM
操作：
classIDetect(nn.Module):
stride =None# strides computed during build
export =False# onnx export
end2end =False
include_nms =Falsedef__init__(self, nc=80, anchors=(), ch=()):# detection layersuper(IDetect, self).__init__()
self.nc = nc # number of classes
self.no = nc +5# number of outputs per anchor
self.nl =len(anchors)# number of detection layers
self.na =len(anchors[0])//2# number of anchors
self.grid =[torch.zeros(1)]* self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl,-1,2)
self.register_buffer('anchors', a)# shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl,1,-1,1,1,2))# shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na,1)for x in ch)# output conv# 初始化隐性知识
self.ia = nn.ModuleList(ImplicitA(x)for x in ch)
self.im = nn.ModuleList(ImplicitM(self.no * self.na)for _ in ch)defforward(self, x):# x = x.copy() # for profiling
z =[]# inference output
self.training |= self.export
for i inrange(self.nl):# 加入隐性知识
x[i]= self.m[i](self.ia[i](x[i]))# conv
x[i]= self.im[i](x[i])
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i]= x[i].view(bs, self.na, self.no, ny, nx).permute(0,1,3,4,2).contiguous()ifnot self.training:# inferenceif self.grid[i].shape[2:4]!= x[i].shape[2:4]:
self.grid[i]= self._make_grid(nx, ny).to(x[i].device)
y = x[i].sigmoid()
y[...,0:2]=(y[...,0:2]*2.-0.5+ self.grid[i])* self.stride[i]# xy
y[...,2:4]=(y[...,2:4]*2)**2* self.anchor_grid[i]# wh
z.append(y.view(bs,-1, self.no))return x if self.training else(torch.cat(z,1), x)
推理时
在模型推理阶段，将
ImplicitA-Conv-ImplicitM
融合为一个1*1的
Conv
操作：
# 将隐性知识与Detect层的1*1卷积进行融合deffuse(self):print("IDetect.fuse")# fuse ImplicitA and Convolutionfor i inrange(len(self.m)):
c1, c2, _, _ = self.m[i].weight.shape
c1_, c2_, _, _ = self.ia[i].implicit.shape
self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1, c2),
self.ia[i].implicit.reshape(c2_, c1_)).squeeze(1)# fuse ImplicitM and Convolutionfor i inrange(len(self.m)):
c1, c2, _, _ = self.im[i].implicit.shape
self.m[i].bias *= self.im[i].implicit.reshape(c2)
self.m[i].weight *= self.im[i].implicit.transpose(0,1)
References
[1] 深入浅出 Yolo 系列之 Yolov7 基础网络结构详解
[2] 【yolov7系列】网络框架细节拆解
[3] yolov7-GradCAM
标签：
人工智能
本文转载自: https://blog.csdn.net/weixin_43799388/article/details/126164288
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版权归原作者 嗜睡的篠龙 所有， 如有侵权，请联系我们删除。